Systems and Methods of Clinical Tracking

ABSTRACT

The clinical analytics platform automates the capture, extraction, and reporting of data required for certain quality measures, provides real-time clinical surveillance, clinical dashboards, tracking lists, and alerts for specific, high-priority conditions, and offers dynamic, ad-hoc quality reporting capabilities. The clinical informatics platform may include a data extraction facility that gathers clinical data from numerous sources, a data mapping facility that identifies and maps key data elements and links data over time, a data normalization facility to normalize the clinical data and, optionally, de-identify the data, a flexible data warehouse for storing raw clinical data or longitudinal patient data, a clinical analytics facility for data mining, analytic model building, patient risk identification, benchmarking, performing quality assurance, and patient tracking, and a graphical user interface for presenting clinical analytics in an actionable format. The clinical informatics platform may enable a method of clinical tracking that includes analyzing the healthcare data to obtain at least one report, and presenting the report in a graphical user interface, wherein the report can be customized based on a criterion.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of the following provisionalapplications, each of which is hereby incorporated by reference in itsentirety:

U.S. Application Ser. No. 61/245,581, filed Sep. 24, 2009; and U.S.Application Ser. No. 61/249,305, filed Oct. 7, 2009.

BACKGROUND

1. Field

The present invention relates to clinical informatics, a clinicalinformatics platform and managing networks of health care providers.

2. Description of the Related Art

Health care systems are evolving at an unprecedented pace. While muchremains uncertain, one thing is clear: knowledge is one key to successin times of change and uncertainty. The ability to meaningfully capture,report, and use data to deliver better and more cost-effective healthcare is critical. Value-based purchasing is making clinical performanceimprovement more important than ever. Success requires the ability toconnect knowledge with action to improve performance.

There remains a need for a clinical informatics platform that automatesthe capture, extraction, and reporting of data required for certainquality measures; provides real-time clinical surveillance, clinicaldashboards, tracking lists, and alerts for specific, high-priorityconditions; provides improved methods and systems for analyzing andmanaging health care referral networks; and offers dynamic, ad-hocquality reporting capabilities.

SUMMARY

The clinical analytics platform automates the capture, extraction, andreporting of data required for certain quality measures; providesreal-time clinical surveillance, clinical dashboards, tracking lists,and alerts for specific, high-priority conditions; provides improvedmethods and systems for analyzing and managing health care referralnetworks; and offers dynamic, ad-hoc quality reporting capabilities.

In an aspect of the invention, a method of clinical tracking may includegathering healthcare data from a plurality of sources, processing thehealthcare data, wherein processing comprises identifying, mapping andnormalizing healthcare data elements, wherein the mapping comprisesassigning data to a field of a database according to a hierarchicallyorganized lexicon of healthcare data elements, wherein multiple dataelement entries in the lexicon are mapped to a single field for at leastone field, analyzing the healthcare data to obtain at least one report,and presenting the report in a graphical user interface, wherein thereport can be customized based on a criterion. The report may identifyat least one risk relevant to at least one patient based at least inpart on the gathered healthcare data. The report may include an alertrelating to at least one risk associated with at least one patient basedat least in part on the gathered healthcare data, such alert presentedin at least one of an audible or visual manner. The report may includean alert identifying at least one patient care error and at least onerecommendation for correcting such at least one error. The report mayinclude instructions for the manner in which one or more healthcareproviders are to provide care to one or more patients based at least inpart on the gathered healthcare data. The report may identify adisparity between the available healthcare resources and the patientneeds identified based at least in part on the gathered healthcare data.The report may identify a high-cost patient based at least in part onthe gathered healthcare data. Processing the healthcare data also mayinclude validating the healthcare data elements. The data may begathered on a periodic basis. The data may be gathered on a real-timebasis, the report may include instructions for the manner in which oneor more healthcare providers are to provide care to one or more patientsbased at least in part on the gathered healthcare data and the report isupdated on a real-time basis. The real-time basis may be at least asfrequent as every five minutes. The graphical user interface may bepresented via a software-as-a-service architecture. The report mayrelate to at least one of a patient, a medical care protocol, anoutcome, a demographic, a behavioral risk factor, a disease risk factor,a procedure, a therapeutic, a therapeutic over a given time period, arisk level, a cost, an admission information, a utilization, readmissioninformation, mortality, and a complication. The criterion may include atleast one of a patient name, an issue, a physician, a location, a due bytime for care or therapy, a risk level, a clinical measure, a procedurecompleted and an image taken.

In an aspect of the invention, a method of optimizing a healthcareresource plan may include gathering healthcare data relating to aplurality of patients from a plurality of sources, wherein the data aregathered on a periodic basis, processing the healthcare data, whereinprocessing may include identifying, mapping and normalizing healthcaredata elements, wherein processing is repeated when new healthcare datamay be gathered, wherein the mapping may include assigning data to afield of a database according to a hierarchically organized lexicon ofhealthcare data elements, wherein multiple data element entries in thelexicon are mapped to a single field for at least one field, analyzingthe healthcare data to obtain at least one patient risk identificationand patient tracking report, wherein analyzing is repeated when newhealthcare data may be gathered and processed, and preparing ahealthcare resource plan for care of the plurality of patients andoptimizing the healthcare resource plan based on the data contained inthe at least one patient risk identification and patient trackingreport. The periodic basis may be in real-time. The real-time basis maybe at least as frequent as every five minutes. Processing the healthcaredata also may include validating the healthcare data elements. Themethod may further include re-optimizing the healthcare resource planwhen new healthcare data may be gathered, processed, and analyzed. Themethod may further include re-optimizing the healthcare resource planwhen a manual change is made to an element of the plan. The trackingreport may relate to at least one of a patient, a medical care protocol,an outcome, a demographic, a behavioral risk factor, a disease riskfactor, a procedure, a therapeutic, a therapeutic over a given timeperiod, a risk level, a cost, an admission information, a utilization,readmission information, mortality, and a complication. Patients at riskmay be automatically detected by the analysis and an alert is generatedidentifying such patients. High-cost patients may be automaticallydetected by the analysis and an alert is generated identifying suchpatients. The healthcare resource plan may be presented in a graphicaluser interface via a software-as-a-service architecture.

In an aspect of the invention, a method of comparative healthcarebenchmarking may include gathering healthcare data from a plurality ofsources, processing the healthcare data, wherein processing may includewherein processing may include identifying, mapping and normalizinghealthcare data elements, wherein the mapping may include assigning datato a field of a database according to a hierarchically organized lexiconof healthcare data elements, wherein multiple data element entries inthe lexicon are mapped to a single field for at least one field,analyzing the healthcare data to obtain at least one of a clinical,operational and financial benchmark, repeating the steps of gathering,processing, normalizing, and analyzing to obtain a data sample tocompare with the at least one clinical, operational, or financialbenchmark, wherein at least one change is made in at least one of therepeated steps, and presenting the data sample with the benchmark as areport in a graphical user interface, wherein the report can becustomized by at least one of changing at least one criterion. The datamay be gathered on a periodic basis. The data may be gathered on areal-time basis, such as at least as frequent as every five minutes. Theplurality of sources may include sources relating to differentgeographic regions. The plurality of sources may include sourcesrelating to different healthcare facilities. The plurality of sourcesmay include sources relating to a specified geographic region.Processing the healthcare data also may include validating thehealthcare data elements. The method may further include linking thehealthcare data elements over time to form a longitudinal data record.The graphical user interface may be presented via asoftware-as-a-service architecture. The at least one criterion may be adata source, a time period, a chart type, a time interval for display, atime interval for analysis, a filter, a hospital, a physician, apatient, a patient characteristic, a cohort, a disease, a gender, an agegroup, a treatment, a payer type and an insurance provider.

In an aspect of the invention, a benchmarking and comparative analyticsdashboard may include a clinical informatics facility, including a dataextraction facility that gathers clinical data from numerous sources, adata mapping facility that identifies and maps key data elements andlinks data over time, wherein the mapping may include assigning data toa field of a database according to a hierarchically organized lexicon ofhealthcare data elements, wherein multiple data element entries in thelexicon are mapped to a single field for at least one field, a datanormalization facility to normalize the clinical data, a flexible datawarehouse for storing raw clinical data or longitudinal patient data,and a clinical analytics facility for data mining and analytic modelbuilding, a user selectable dashboard definer configured to provide userselectable options for defining the clinical analytics to be presentedin a report at a dashboard, and a display definer configured to operatein conjunction with the user selectable dashboard definer to define theformat in which the clinical analytics report from the clinicalinformatics facility is to be presented at the dashboard. The data maybe gathered on a periodic basis. The data may be gathered on a real-timebasis. The real time basis may be at least as frequent as every fiveminutes. The data normalization facility may de-identify the data. Themethod may further include validating the clinical data. The clinicalanalytics facility may enable patient risk identification and patienttracking. The selectable options may include the addition of acomparative benchmark. The selectable options may enable comparison toat least one of another patient, healthcare provider, doctor, healthcarefacility, hospital, disease, condition, gender and age group. Theselectable options may include the addition of a patient riskidentification and patient tracking report relating to at least one of apatient, medical care, an outcome, a demographic, a behavioral riskfactor, a disease risk factor, a procedure, a therapeutic, autilization, a readmission, mortality, and a complication. The format ofthe report may include at least one of a table, a chart, text, and agraph and the format may be customized based on at least one of a datasource, a time period, a chart type, a time interval for display, a timeinterval for analysis, a filter, a hospital, a physician, a patient, apatient characteristic, a cohort, a disease, a gender, an age group, atreatment, a payer type and an insurance provider. The dashboard may bepresented via a software-as-a-service architecture.

In an aspect of the invention, a method of ingesting and analyzinghealthcare data from a plurality of data sources in real-time mayinclude connecting to at least one data source, retrieving data from thedata source on a periodic basis to a database, synchronizing databetween the at least one data source and the database, processing thedata to identify data elements, map data elements, and normalize dataelements, wherein the data elements are stored in a database, whereinthe mapping may include assigning data to a field of a databaseaccording to a hierarchically organized lexicon of healthcare dataelements, wherein multiple data element entries in the lexicon aremapped to a single field for at least one field, linking the dataelements over time to form a longitudinal data record, wherein thelongitudinal data records are stored in a longitudinal data warehouse,and analyzing the at least one of the data elements and data records toobtain at least one of actionable clinical analytics, a patient riskidentification, a disease-specific analytic model, a predictive model, abenchmark and a quality measure. The periodic basis on which data areretrieved may be real-time. Real-time may be at least as frequent asevery five minutes. The at least one data source may include doctor'snotes from which data may be retrieved using natural processinglanguage. The at least one data source may include at least one of anelectronic medical record, an electronic health record, ambulatoryclinical data, claims data, paid claims data, adjudicated claims data,inpatient clinical data, pharmacy data, doctor's notes, self-reporteddata, census data, telemetry data, a networked monitor, a home bloodpressure device, a home health monitoring device, a sensor device,mortality data, an internal management system, a hospital inventorysystem, a clinical inventory system, a clinical guideline, a specialtymanagement system and an order set. Processing the healthcare data alsomay include validating the healthcare data elements. The at least one ofactionable clinical analytics, a patient risk identification, adisease-specific analytic model, a predictive model, a benchmark and aquality measure may be presented in a graphical user interface via asoftware-as-a-service architecture.

In an aspect of the invention, a clinical informatics platform mayinclude a data extraction facility that gathers clinical data fromnumerous sources on a periodic basis, a data mapping facility thatidentifies and maps key data elements and links data over time, whereinthe mapping may include assigning data to a field of a databaseaccording to a hierarchically organized lexicon of healthcare dataelements, wherein multiple data element entries in the lexicon aremapped to a single field for at least one field, a data normalizationfacility to normalize the clinical data, a flexible data warehouse forstoring at least one of the raw clinical data and longitudinal patientdata, a clinical analytics facility for data mining, analytic modelbuilding, patient risk identification, and patient tracking, and agraphical user interface for presenting clinical analytics in anactionable format. The periodic basis on which data are gathered may bein real-time. Real-time may be at least as frequent as every fiveminutes. The numerous sources may include doctor's notes from which datamay be retrieved using natural processing language. The numerous sourcesmay include at least one of an electronic medical record, an electronichealth record, ambulatory clinical data, claims data, paid claims data,adjudicated claims data, inpatient clinical data, pharmacy data,doctor's notes, self-reported data, census data, telemetry data, anetworked monitor, a home blood pressure device, a home healthmonitoring device, a sensor device, mortality data, an internalmanagement system, a hospital inventory system, a clinical inventorysystem, a clinical guideline, a specialty management system and an orderset. The data normalization facility may de-identify the data. Themethod may further include validating the clinical data. The graphicaluser interface may be presented via a software-as-a-servicearchitecture.

In another aspect of the invention, a computer readable medium havingcode which implements a method for describing, evaluating,understanding, or managing a network of health care providers, themethod may include constructing a referral network database ofphysicians and health care providers from at least one of a private anda public data source, extracting data pertaining to shared patients orreferrals between the physicians and health care providers from adatabase, and generating a graphical representation of referral patternsin the referral network of physicians and health care providers, whereinat least one element of the graphical representation depicts a measureof an extent of a type of activity within the referral network. Theelement of the graphical representation may use at least one of size,thickness, color and pattern to depict a type of activity. The elementof the graphical representation may depict how many patients are sharedamong at least two health care providers. The medium may furthercomprise analyzing the referral patterns in the graphical representationto examine characteristics of the practice of the network and to enablemanaging the network of health care providers. The step of constructinga referral network of physicians and health care providers may use datamining techniques to find relationship data between physicians andhealth care providers. The step of constructing a referral network ofphysicians and health care providers may identify physicians and healthcare providers as nodes with linkages in a referral network. The datasources may include automated collection and user-generated data sourcesfor referral network construction. The user-generated data may be from asurvey. The data pertaining to shared patients or referrals may beextracted from a claims or electronic health record database. Thegraphical representation may be an x-y coordinate system, an xyzcoordinate, a pie chart, a radar display, a GIS map, and other non-xyplots. Groups of physicians and health care providers may bedifferentiated in the graphical representation by at least one of acolor, a shape, a shading, and a size. The size of the objectrepresenting the physicians or health care providers in the graphicalrepresentation may correlate with a metric. The metric may be at leastone of cost, quality of care, compliance, or other measure of medicalcare, cost, resource use, quality or patient outcome.

These and other systems, methods, objects, features, and advantages ofthe present invention will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings.

All documents mentioned herein are hereby incorporated in their entiretyby reference. References to items in the singular should be understoodto include items in the plural, and vice versa, unless explicitly statedotherwise or clear from the text. Grammatical conjunctions are intendedto express any and all disjunctive and conjunctive combinations ofconjoined clauses, sentences, words, and the like, unless otherwisestated or clear from the context.

BRIEF DESCRIPTION OF THE FIGURES

The invention and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1a depicts a block diagram of the clinical analytics platform.

FIG. 1b depicts a workflow of the clinical analytics platform.

FIGS. 2a-2b depict a benchmarking and analytics tool of the clinicalinformatics platform.

FIGS. 3a-3b depict a data processing and clinical surveillance tool ofthe clinical informatics platform.

FIG. 4 depicts a heat map of a daily encounter volume.

FIG. 5 depicts a heat map of diabetes co-morbidity.

FIG. 6 depicts a heat map for diabetes prescribing patterns.

FIG. 7 depicts a parallel coordinate plot of patients with a decreaseof >1% in Hemoglobin A1c.

FIG. 8 depicts a parallel coordinate plot of patients with an increaseof ≧1% in Hemoglobin A1c.

FIG. 9 depicts a parallel coordinate plot profiling change in HemoglobinA1c.

FIG. 10 depicts only those patients who had greater than or equal tofive endocrinology encounters on a parallel coordinate plot.

FIG. 11 depicts those patients who had an endocrinology encounter on aparallel coordinate plot.

FIG. 12 depicts a plot of physicians treating diabetes by outcome andresource utilization.

FIG. 13 depicts a heat map of doctors with 10+ actively managed diabetespatients.

FIG. 14 shows a visual representation of interactions in a primary carephysician network.

FIG. 15 shows a visual representation of interactions among primary carephysicians and endocrine specialists in a referral network.

FIG. 16 shows another visual representation of interactions amongprimary care physicians and endocrine specialists in a referral network.

FIG. 17 shows a visual representation of primary care and endocrine careproviders in a referral network.

FIG. 18 depicts a logical flow for a computer-implemented method ofmanaging health care providers in a referral network.

FIG. 19 depicts the output of an AMI detection algorithm.

FIG. 20 depicts a COAG Risk group tracking dashboard.

FIG. 21 depicts a network topology.

FIG. 22 depicts a block diagram of the data life cycle.

DETAILED DESCRIPTION

The clinical analytics platform automates the capture, extraction, andreporting of data required for certain quality measures, providesreal-time clinical surveillance, clinical dashboards, tracking lists,and alerts for specific, high-priority conditions, provides improvedmethods and systems for analyzing and managing health care referralnetworks, and offers dynamic, ad-hoc quality reporting capabilities.Throughout this specification, real-time indicates that an action istaken in an interval of time such that the data that are available tothe platform 100 are data that are current as of an interval of time notfar from the current time. The interval can vary from a few hours orminutes, such as five minutes, all the way to instantaneous.

The clinical informatics platform may empower health care,pharmaceutical and biotechnology firms, medical device manufacturers,government agencies, and financial services firms with insight into howto manage provider networks and provider network shared patients orreferrals, how patient populations are treated, which treatments andprocedures are prescribed, and importantly, the quality, efficacy, andcost of this care. The clinical informatics platform may assemble,standardize, and analyze clinical, operational, social network,referral, insurance and financial data across varied treatment settingsand time periods to generate a longitudinal, comprehensive view ofpatient care. The clinical informatics platform may address the specificneeds of inpatient and outpatient health care providers, pharmaceuticaland biotechnology firms, medical device manufacturers, governmentagencies, and financial services firms by combining deep, retrospectivecapabilities with powerful real-time predictive tools that connectknowledge with action.

The clinical informatics platform may enable organizations to transforman immense reservoir of data into valuable, actionable knowledge using acomprehensive suite of software-as-a-service (SaaS) solutions thatunlock the clinical information needed to improve patient care whileimproving financial performance. The SaaS-based clinical informaticsplatform applies sophisticated techniques to mine, standardize, validateand/or aggregate health care data from disparate IT systems, all withina state-of-the-art, HIPAA-compliant, and highly secure environment. Theclinical informatics platform analyzes clinical, operational, socialnetwork, referral, insurance and financial data and delivers powerfulanalytic insights and comparative benchmarks with cost-effective,retrospective, and real-time SaaS-based tools. The SaaS-based toolsenable delivering real time comparative analytics without the end userhaving to purchase or maintain any additional hardware or humanresources and includes rapid, scalable, data extraction, mapping, andontological normalization systems. The clinical informatics platform maycombine both retrospective, deep-dive analytic systems, such as thebenchmarking and analytics tool 202 or the data processing and clinicalsurveillance tool 302, with real-time data processing capabilities. Inembodiments, the platform may be modular and contain all available toolsor only certain tools. The clinical informatics platform may includedisease-specific analytic tools, predictive models, and modules. Theclinical informatics platform may include or enable the generation ofdetailed, customizable clinical, operational, social network, referral,insurance and financial benchmarks. The clinical informatics platformmay support collaborative development and testing of performance andoperational improvement strategies within and among organizations.

The historic barriers to leveraging health care, referral, insurance andsocial networking data are many: data reside in many different systems,data are trapped in local terminologies and free text, robust clinicalmodels require costly tools and large samples, and real-time clinicalanalytics are costly and difficult-to-use, to name a few. The needs areclear: extract all the data, normalize all the data, provide robustclinical and networking analytics, and deliver powerful and timelyinsights. The clinical analytics platform meets these needs: it hasflexible, platform-agnostic data extraction capabilities, providesscalable data normalization and next-generation natural languageprocessing (NLP), a singular, relational longitudinal patient datawarehouse, real-time predictive analytics, modeling, patient trackingtools, social networking and referral analysis tools, and clinicalchecklists. With the tools available in the clinical informaticsplatform, users may: gain valuable insight into the clinical andoperational performance of an organization; conduct real-time andretrospective analytics and benchmark clinical performance; and employdisease-specific clinical analytics and evidence-based data to intervenein a timely manner to identify patients at risk, reduce morbidity,mortality, and complications in real-time, ensure that opportunities forimprovement are identified before the patient has left the care setting,and, and manage provider networks and provider network shared patientsor referrals, and otherwise effect positive change.

An understanding of all aspects of clinical and operational performance,such as the quality, safety, and cost of healthcare provider care, maybe enabled by the clinical informatics platform. The health careprovider may be enabled to act in real-time to ensure delivery of thebest and most efficient care. Health care providers may be enabled tocompare, analyze, and identify best practices, and then collaborate withpeers around the development and dissemination of best practices and tooptimize performance-based reimbursement. The clinical informaticsplatform may connect cost and outcomes to maximize cost-effective care,identify and track the care of high-cost and high-risk patients inreal-time to reduce preventable complications and/or never events, andimprove performance on Joint Commission (JCAHO) and pay-for-performancemeasures, and prove the value of the care delivered to payers withcustomizable, comparative performance benchmarks. Health care providersmay be empowered to qualify for certain government and private programs,such as the American Recovery and Reinvestment Act (ARRA) funding.

Referring to FIG. 1a , a block diagram of the clinical analyticsplatform 100 is shown. A data extraction facility 104 can extract datafrom a plurality of disparate, healthcare and claims data sources 118 toenable the real-time collection, processing and centralized storage ofhealth records in a database. Data ingestion techniques may be appliedto a heterogynous system of EMRs from various vendors and systems toobtain data, normalize them and store the newly processed data in ahomogenous database where a single set of applications can be used tointerface with and analyze the data. Real-time, continuous dataingestion may come from various data sources 118 which may includeambulatory clinical data, pharmacy data, doctor's notes, EHRs, EMRs,inpatient clinical data, biographical data, hospital billing data,claims data, census data, self-reported data, networked devices andmonitors (e.g. blood pressure device, glucose meter, etc.), mortalitydata, telemetry, inventory systems, clinical guidelines, managementsystems, order sets, and the like. For example, NLP techniques can beused to gather data from doctor's notes or other transcriptions, bothnumeric and text data. Data may be extracted, optionally encrypted, andingested by the system in a running load process. Data may be obtainedthrough an RSS feed or a transmission or extraction of data in a formatsuch as XML, HL7, SCRIPT, X12, CSV, HL7v2, HL7v3, Dicom, X12N, NCPDP,and the like. Extract, Transform, and Load (ETL) tools may be used toconnect remote databases to the platform 100 and pull data out of theremote databases so that the data goes from database to database. Thismethod speeds things up and requires less hardware since a copy of thedata does not have to be written for transmission or extraction.

The platform 100 enables ingestion and semantic normalization of thehealthcare data by converting the data in the healthcare records tostandardized data elements using a data normalization facility 110 andmapping the converted data with standard terminologies, such as FederalHealth Architecture (FHA) terminologies, billing codes, IDC codes, CPTcodes and the like using a mapping application of the data processor108. The data processor 108 may transform data from the various formatsin which it exists. Data may be mapped iteratively against divergentsource systems. Mapping data may take advantage of standard and customterminologies and combinations thereof. For example, the terminologiesmay enable identifying data elements by the various ways they may bedescribed in different data sources and mapping all of the disparateelements to a single terminology used by the platform 100. In anotherembodiment, mapping may be ontological, that is, the terminologies mayhave a hierarchy. For example, 5 different variables may be found in asingle or a plurality of data sources. In choosing which target variableof the platform to map the 5 variables to, a terminology may beconsulted. Multiple possibilities may exist in the terminology, but ahierarchy of the terminologies may facilitate choosing which targetvariable of the platform to use. A rules database 112 may be used forstoring terminologies, codes, hierarchies, rules for datade-identification, and the like. The rules database 112 may be updatedperiodically as new terminology becomes available or updated. The rulesdatabase 112 may provide rules to the data processor 108 for mapping.The rules database 112 may also store rules, attributes,characteristics, and criteria that are used in each analytic model.

Data may be linked over time to create longitudinal patient records.Data may also be linked along the lines of cohorts, practice groups,geographic areas, and the like. The data may be subject to validation.Validation may include identifying and omitting outlier values from thedata, removing unreliable data and the like.

The data may be stored in a flexible data warehouse, such as a raw datastore 118, data mart 120 or a longitudinal patient data warehouse 114.

The data may be analyzed by the data processor 108. Since the data maybe real-time or near real-time, the analysis can enable providing careinstructions, flagging medication and/or care errors, flagging eventsfor follow-up or treatment, making recommendations, supporting diseasemanagement, cost containment, generate epidemiological/bioterrorismalerts, and the like. Real-time data ingestion, processing, and analysisenables automating processes and generating and updating care plans innear real time. The data may be certified. Interfaces to the platform100, such as a user interface 122, report facility 124 audit facility128, and other interfaces 130, may be used to search and view data,initiate analyses, visualize data, generate reports, generate a trackingpage, and the like.

In a workflow of the clinical informatics platform as shown in FIG. 1b ,data, such as ambulatory clinical data, financial data, inpatientclinical data, pharmacy data, and the like, may be gathered and/orextracted from source systems. The extracted data may undergomanipulations, such as mapping and normalization prior to storage in adatabase. The data may then be analyzed, tracked, manipulated and thelike by any number of clinical analytics tools. The analytics may bemodular, such as by disease, condition, cohort, patient area, geographicarea, therapeutic protocol, practice group, hospital, and the like. Theanalytics may generate granular comparative data. The analytics mayenable predictive modeling and understanding of the cost and efficiencyof care. The analytics may enable quality measures, such as PQRI andHEDIS registry reporting. For example, a clinical analytics tool mayenable analytic data grouping. FIG. 22 depicts a block diagram of thedata life cycle including the following steps: pre-extraction whereinventory is taken of systems and the best extraction approach isidentified, data extraction, processing, mapping, ingestion,normalization, validation, analytics, and data certification, such asmedical validation, QA, analytics and general validation.

In an embodiment, the platform 100 may comprise tools for analysis anddata presentation and reporting. Certain tools may enable near real-timequality/risk identification and workflow. Tools may enabledisease-specific analytic models. Tools may enable data mining, such asto identify patients at risk. In any of these tools, the analytics maybe presented as an actionable visualization that may highlight variance.The presentation may include patient, physician, group views, and thelike. The data presentation may be a collaboration platform. The datapresentation may include real-time alerts, such as alerts relating to atleast one risk associated with at least one patient based at least inpart on the gathered healthcare data. Alerts may be presented in atleast one of an audible or visual manner. In an embodiment, datapresentation may be flash-based or involve some other dynamic mediaand/or animation.

Referring to FIG. 2, the clinical informatics platform may comprise atool for enabling robust clinical, operational, and financialbenchmarking and comparative analytics across the continuum of healthcare. The benchmarking and analytics tool 202 may be a dashboard forpresenting comparative analytics. Data from disparate data sources 118are extracted as described herein, normalized and mapped as describedherein, then analyzed to obtain a benchmark and a data sample to compareto the benchmark. In some embodiments, the benchmark is known and doesnot have to obtained through analysis.

The analytics may be presented in a number of report formats, such astables and graphs. The graphs may be of any format, such as bar graph,pie chart, scatter plot, line graph, and the like. The graphs may becustomized using a number of built-in features of the tools, such as bychanging the data source, the time period for analysis, the chart type,time intervals for display, a custom or built-in filter, a comparison toanother subject (such as hospital, physician, disease, gender, agegroup, and the like), and the like. A graphical user interface to theplatform 100 may be used to present the comparative data and thebenchmark as a report.

For example, the chart 202 in FIG. 2a , shown expanded in FIG. 2b ,shows medical care analytics in the field of utilization. FIG. 2bdepicts a chart that compares the mean length of stay (LOS) of patientson regular human insulin with pressure ulcer stages III and IV, with themean hospital LOS on the y-axis and the time period on the x-axis. Inthis graph, the mean LOS for one hospital is compared to a regionalaggregate hospital benchmark. A filter may be applied to this chart,such as by using a filter wizard. For example, the data may be filteredby insurance provider so that LOS is displayed for particular insuranceproviders, such as MEDICARE, MEDICAID, MEDICARE and MEDICAID, privateinsurance, government-sponsored insurance, and the like. To enable adifferent comparison, a comparison wizard may be employed. For example,a Charlson co-morbidity index comparison may be requested for the data.Instead of having a single data point, the data may be presented foreach time interval by Charlson index or by range of Charlson indices. Inanother example, the data may be compared to another hospital includingto or instead of the benchmark. In yet another example, the data may bepresented at a more granular level, such as by attending physician,hospital floor, hospital unit, hospital bed, procedures, and the like.By providing different ways to present and manipulate data, patterns andoutliers may be more readily identified. The visual dashboard provides anumber of benefits. It enables real-time clinical intervention andreduces cost, morbidity, and mortality. It gathers, maps, and normalizesdata in near real-time to predict and track which patients are likely tobe high-risk and/or high cost and to alert for compliance to JointCommission Core Measure metrics.

Referring to FIG. 3a , the clinical informatics platform may alsocomprise a dashboard for a near real-time data processing and predictiveclinical surveillance system that identifies high-risk, high-costpatients, tracks necessary care, and supports clinicians to intervene toimprove care. The data processing and clinical surveillance tool 302 maybe a dashboard for presenting data processing and clinical surveillanceand enabling real-time predictive risk tracking. Data from disparatedata sources 118 are extracted as described herein, normalized andmapped as described herein, then analyzed to obtain clinical trackingdata that can be presented by a tool 302 in a graphical user interfaceof the platform 100. An embodiment of a report is shown in an expandedversion in FIG. 3b . A reports tab may enable a user to generate reportsrelated to a number of topics, such as the patient, medical care, andoutcomes, and sub-topics, such as demographics, behavioral risk factors,disease risk factors, procedures, therapeutics, utilization, ICU,readmission, mortality, complications, and the like. Since the data arereal-time, real-time clinical intervention is enabled as patients whoare high-risk and/or high-cost are more readily identifiable and easy totrack. The screen in FIG. 3b displays a ‘Tracking’ tab of the tool 302that shows active tracked patients in a hospital unit who are in theacute myocardial infarction (AMI) risk group. Demographic information isavailable for each patient as well as therapeutics over a given timeperiod, risk level, physician, location, arrival date/time, and thelike. The tool 302 provides a way to manually include or excludepatients from tracking. A reason may need to be recorded for exclusionor inclusion. The reason may be selected from a list of standard reasonsor manually entered or entered in some other way. Filters may be appliedto the data presentation. In an ‘Issues’ tab, issues, such as ‘all openissues’ may be shown for patients being tracked. The ‘Issues’ tab mayshow Patient name, Issue, Physician, Location, Due By timer, and thelike. Filters may be applied to this display. For example, patients inonly certain locations may be included in the listing, or in otherembodiments, patients in all locations may be included. In anotherexample, patients in only certain risk groups may be included in thelisting, or in other embodiments, patients in all risk groups may beincluded. When a patient is selected, their profile may be displayed.For example, patient information may be shown, their AMI risk profilemay be shown, or some other risk profile may be shown. In an example,the AMI risk profile may include statuses over time, such as location,blood pressure, temperature, anticoagulants, beta blockers,thrombolytics, CK-MB, triglyceride levels, CBC, glucose level, troponinlevels, images taken, procedures done, and the like. A patient may bemanually excluded or included in a risk group, but a reason may need tobe recorded. The reason may be selected from a list of standard reasonsor the reason may be manually entered. Given a patient's risk profilewhich is known in real-time, predictive analytics may be applied toidentify patients at risk. Patients at risk may be automaticallydetected by the tool 302, such as by using the AMI detection algorithmtool shown in FIG. 19. The visual dashboard of the tool 302 provides anumber of benefits. The platform 100 enables “at a glance” status checkson the floor, automates and optimizes identification of patients to betracked, reduces the number of patients to be tracked, connectsknowledge to action, provides clinical data for better understanding,compresses “time to intervention” for better outcomes, compresses “timeto action” for core measure compliance, reduces cost, morbidity, andmortality, and the like. FIG. 20 depicts another example of a clinicalsurveillance dashboard for a risk group of patients on anticoagulationmedications.

In an embodiment, the clinical surveillance dashboard may enable ahealth care provider or health practitioner to see each patient'scountdown to events that need to be done within a certain period oftime, such as within an hour of admission, day of admission, and thelike. The health practitioner's plan for care can be viewed by doctor,patient, floor, clinic, disease, and the like, along with all of therelevant data and measures that went into establishing the plan. Theplan for care itself may be automatically customized based on anindication, therapeutic protocol, and the like. The plan and/or itstimeline for action may be updated in real-time, such as when new databecome available to the platform. The plan for care may be for aparticular patient and may be adjusted based on real-time data regardingthat patient. For example, if the real-time data indicates that thepatient is recovering more slowly than expected, the plan may be revisedto include higher doses of painkillers and more frequent testing andmonitoring.

The clinical informatics platform 100 may also comprise a tool for anear real-time data processing and predictive clinical surveillancesystem that identifies diabetic patients. Data from various sources,such as laboratory data and pharmacy data, may be analyzed using analgorithm to determine if a patient may be diabetic, based on some knowncombination of laboratory and pharmacy data that indicates a highlikelihood of the pathology.

The clinical informatics platform 100 may also comprise a tool for anear real-time data processing and predictive clinical surveillancesystem that identifies cohorts of patients that fit the JCAHOguidelines.

The clinical informatics platform 100 may also comprise tools foranalytic model building. For example, to build a disease model, aspectsof the disease that might be of interest in determining the quality,cost, and/or outcome of care may be obtained from the literature,textbooks or know how. These aspects may be defined as inputs to themodel in terms of rules, attributes, characteristics, criteria, or thelike. These inputs may be defined in a rules database 112 and updatedperiodically or as needed. Data may be analyzed according to the modelby the platform 100 to enable determining a disease state. For example,a diabetes model may be consulted to determine or predict if a patienthas diabetes. The model may require that certain data be available, suchas diagnoses codes, glucose test results, HgbAlC levels, outpatientprescriptions, and the like. These data may be analyzed according torules of the model. For example, the model may indicate that a patientis diabetic if a glucose level is over a prescribed amount and if anHgbAlC level is over a prescribed amount. If the actual glucose level isbelow the prescribed amount and the HgbAlC level is above the prescribedamount, when these data are input to the model, it may be determinedthat there is a moderate likelihood that the data corresponds to apatient with diabetes. Other disease specific models may be enabled bythe platform 100, such as models for congestive heart failure,hypertension, COPD, dyslipidemia, coronary artery disease, peripheralvascular disease, acute myocardial infarction, cerebrovascular disease,stroke, renal failure, osteoarthritis, rheumatoid arthritis, ulcer,depression, heart failure, pneumonia, septicemia, adult preventativescreening, CAD, adult asthma, pediatric asthma, chronic kidney disease,anti-coagulation/VTE, fibromyalgia, back pain, obesity, osteoporosis,estrogen-related disorders, inflammatory bowel syndrome, dementia, BPH,pain management, immune disorders, HIV, colon cancer, prostate cancer,breast cancer, pneumonia, TB, anemia, lupus, gout, thyroid disorders,hepatitis, atrial fibrillation, arrhythmias, and the like.

The clinical analytics platform 100 may support application programminginterfaces (APIs) integrated with the data warehouse 114 to allow thedevelopment of applications that can leverage the normalized data, suchas for applications directed to regional healthcare issues, individualhealth providers, research or clinical studies, pharmaceutical andbiotechnology companies, and the like.

The clinical analytics needs of ambulatory providers may besignificantly different from those of acute care providers. Care inambulatory environments may occur over a longer time period withmultiple discrete events. These events may occur in different locations,under the care of multiple providers, and prescribed treatments may onlyshow results over an extended period of time. In addition,documentation, coding practices, disease specificity and multiplicityall combine to make clinical analytics far different from similarefforts in hospital settings. The clinical informatics platform isuniquely designed to handle the specific needs of ambulatory careproviders. The clinical informatics platform integrates clinical,claims, lab, and other data to generate a complete and longitudinal viewof member organizations' patient populations, provides disease-specificclinical classification and advanced analytics to define appropriatepatient cohorts, treatment pathways, outcomes and associated costs,provides treatment effectiveness and outcomes analysis to supportevidence-based process improvements, thus allowing physician practicesto enhance cost-effectiveness while providing the highest qualitypatient care, facilitates comparative analytics and benchmarking byaggregating data from participating medical groups, facilities,practices, and physicians into a single, standard clinical ontology,supports organizations to develop, compare, and share best practices andperformance improvement strategies through our unique collaborativeprograms, and the like. The clinical informatics platform may enableusers to access comprehensive patient data and the data necessary forquality reporting, effectively manage chronic disease patients withprotocol tracking tools and task lists, automate real-time surveillanceand employ clinical decision support at the point of care with real-timereminders and alerts, and the like. The clinical informatics platformmay help organizations demonstrate the value of the care they deliver,receive appropriate compensation for the quality of the care theydeliver, get clinical detail on best practices that lead to improvedoutcomes, improve patient care in a timely manner by combining real-timeclinical surveillance with robust retrospective clinical analytics,attract patients by enhancing their organization's reputation forquality, and the like.

The clinical informatics platform may also enable the activities of lifescience firms. Using the clinical informatics platform, life sciencesfirms may be able to quantify patient populations, market share, andmarket opportunities, all by disease severity and co-morbidities. Theclinical informatics platform may profile patient segments with detailedclinical specificity (e.g. lab results and radiology reports), identifytreatment decisions by physician specialty and practice setting, andelucidate the associated costs and outcomes—information vital togenerating appropriate and tailored marketing strategies and tactics.

The clinical informatics platform may provide life sciences companieswith the tools necessary to understand the clinical drivers of treatmentdecision-making, to quantify their brand's unique benefits, and toaccurately assess a myriad of market opportunities. The clinicalinformatics platform may offer the timeliest and most complete clinicaldata needed to manage and succeed in today's challenging marketplace.The clinical informatics platform's longitudinal clinical data offerunprecedented insight into brand choices and the associated clinicaloutcomes and cost effectiveness. With the clinical informatics platform,life sciences firms can more accurately and expediently quantify patientpopulations, market share, and market opportunities. The clinicalinformatics platform enables profiling patient segments with detailedclinical specificity (e.g., lab results, radiology, co-morbidities),identifying the clinical drivers of treatment decisions by physicianspecialty, and elucidating the costs and outcomes associated withtreatments. The clinical informatics platform may provides longitudinalclinical data needed to gain a more accurate picture of specific patientsub-populations, brand-specific clinical profiles, including lab andradiology results, for improved marketing message development andeffectiveness tracking, clinical evidence for the development of refinedsegmentation strategies, clinical data highlighting which treatmentsoccurred when, and by which specialty, to accurately define the sequenceof care and associated outcomes, and the like. The clinical informaticsplatform clinical data enables life sciences companies to: measureclinical outcomes within specific patient segments, more accuratelyidentify unmet needs and associated market opportunities, tailormarketing messages to accurately address the specific needs of providergroups and patient cohorts, and the like. The clinical informaticsplatform enables users to quickly and accurately answer numerousquestions, such as the following: Am I attracting the right patientsegments based on my product's unique clinical profile?; How does theclinical profile of patients on my brand compare to those of mycompetitors, both branded and generic?; In which patient segments doesmy brand outperform the competition in terms of clinical outcomes andcost effectiveness?; How do I maximize the pricing and reimbursement formy brand?; and the like.

The clinical analytics platform 100 may be deployed in many differentenvironments to provide for data extraction, processing, storage,analysis, and presentation. For example, the platform 100 may bedeployed in ambulatory care facilities, life science firms, acute carefacilities, hospice, clinical trial facilities, insurance companies,senior living facilities, veterinary facilities, epidemiologicalcenters, triage centers, emergency rooms, and the like.

The clinical informatics platform may further be used for social networkanalysis of health care providers, provider network shared patients,referrals and the like. By way of one example, in instances where suchanalysis is desired, the clinical informatics platform may extractvarious data from numerous health care providers that relate to patientmovement and treatment within the network. The clinical informaticsplatform can then normalize the data and apply analytics to the datathat will display patient movement, course of treatment and progress,physician referral, or other results based on the data provided andinformation desired. The network in question and its associated data maybe visualized and integrated into the clinical informatics platform userinterface through the use of a network analysis visualization tool.

Further, the clinical informatics platform may enable social networkanalysis of health care provider, such as primary care physicians andspecialists, interactions which may enable managing provider networksand provider network shared patients or referrals. The social networkanalysis results may be visualized on a coordinate system, such as anx-y coordinate system, an xyz coordinate, a pie chart, a radar display,a GIS map, other non-xy plots, and the like. For example, the Ycomponent of the coordinate system may be the physician and the Xcomponent may be key care variables around the way that care isdelivered in a particular disease. Another coordinate may identify thephysicians by their clinic. The visualization may be examined forphysicians who cluster together by using an algorithm. The clusters maybe indicative of patterns of care that are characteristic of thecluster, and may be suggestive of a pattern of care, cost, or outcomethat is either positive or negative.

Social network analysis for managing provider networks and providernetwork shared patients or referrals may be described with reference toan example involving an internist or specialist network. A first step insocial network analysis in this example may involve identifying all ofthe encounters between a physician, a patient and a medical center tocreate a bi-partite network. For example, connections betweenphysicians, patient and physician, and another physician may berepresented in the network. The bi-partite network may then be condensedinto a doctor-to-doctor network. The doctor-to-doctor network may bemade bi-partite again in that all of the same doctor type to same doctortype connections may be eliminated. The now condensed network may be aninternist to specialist network.

A social network analysis visualization tool may enable visualizing anetwork in many ways, such as by using a coordinate system. For example,groups of providers may be differentiated by a specific color, aspecific shape, or a size. The thickness of the connections, representedas lines, between members of the group may be a measure of how manypatients they share between them. This measurement may be utilized as aweight in the analysis. From this visualization, significant patternsmay become apparent that may enable examination of characteristics ofthe practice of medicine. For example, the social network analysisvisualization may be used to show patients who are on MEDICARE versuscommercial insurance versus government insurance. The analysisvisualization may identify patient encounters for patients that areabove average. The analysis visualization may show a practice clusterthat stands out in their utilization of imaging, patient outcomes, cost,and the like by being able to correlate the clusters and connectionswith various metrics. For example, the analysis may enable a mapping ofthe quality and cost of a network of doctors. In some embodiments, thesize of the objects representing a group may be a visual indicator ofsome kind of measurement, such as how much was spent per visit on apatient. In this embodiment, the social network visualization may showthat some groups practice the same medicine and get different outcomesat the same cost.

The social network analysis visualization tool may enable determiningwho are popular providers and influencers of other providers and careoutcomes, not by examining communication or information flow, but ratherby analyzing actual care characteristics.

Referring to FIG. 14, a visual representation of interactions in aprimary care physician network is shown. Each diamond represents aprimary care physician in the network, or a vertex, and each connectingline between each primary care physician, or the edge between twovertices, represents the number of shared patients by the thickness ofthe line. The visualization shows distinct clusters of physicians whohave many shared patients among them with a smattering of smallerclusters and physicians who do not share patients with any otherphysicians or have very few shared patients. In this example, primarycare physicians may be identified as part of an institution or practiceby a unique shading or coloring of the diamond.

Referring to FIGS. 15 and 16, an example of a social network analysis ofinteractions among primary care physicians and specialists in a referralnetwork is shown. In this example, a visual representation of a socialnetwork analysis of interactions among primary care physicians andendocrine specialists for diabetes mellitus type 2 in a referral networkis shown. FIG. 16 is a close-up view of the referral network in FIG. 15where the isolated physicians and endocrinology specialists have beenremoved for clarity. Each diamond represents a primary care physician inthe network, or a first vertex, each hexagon represents an endocrinologyspecialist, or a second vertex, and each connecting line between eachprimary care physician and endocrinology specialist, or the edge betweentwo vertices, represents the number of shared patients by the thicknessof the line. In this example, primary care physicians may be identifiedas part of an institution or practice by a unique shading or coloring ofthe diamond. The visualization shows a distinct cluster of physiciansand an endocrinology specialist, where a single endocrinology specialistreceives many shared patients or referrals. The visualization also showsphysician or endocrinology specialist clusters where multiplespecialists are referred to by the physicians. Finally, there are alsophysicians displayed who make no referrals, as well as endocrinologyspecialists who receive few or no shared patients or referrals. Thevisualization highlights where certain line thicknesses could beincreased, that is, where more shared patients or referrals can be madebetween physician and endocrinology specialist. The visualization alsoenables correlating metrics, such as expense, care outcomes, compliance,and the like, with a thickness of connection. By identifyingendocrinology specialists by referral, the visualization may suggestwhich specialists to go to and which ones to avoid. In embodiments, thesocial network analysis may be very sensitive for a particularinstitution. For example, the visualization shows that certainendocrinology specialists are members of distinct clusters, where thedistinct clusters are representative of referral networks arising fromdistinct institutions. In this example, the three endocrinologyspecialists within the rectangle on FIG. 16 are all part of the sameclinic but the top-most specialist seems to be a major part of twoclusters in the referral network, while the two other endocrinologyspecialists get few shared patients or referrals from physicians in theother cluster. Thus, the visualization may identify certainpractitioners who are key to the interactions in the referral network orcertain practitioners who are draining referral patients from a certainclinic. The visualization may be examined over time to determine the ebband flow of the referral network. As clusters and connections becomeapparent, they may be actionable, and modifications may be made topractices.

Referring to FIG. 17, a visual representation of primary care andendocrine care providers in a referral network in diabetes mellitus typeI is shown. This visualization is different from that shown in FIGS. 15and 16 in that the disease is different, diabetes mellitus type I is anautoimmune disease while diabetes mellitus type 2 is a disorder that ischaracterized by high blood glucose in the context of insulin resistanceand relative insulin deficiency. By examining the differences in thevisualizations, a comparison can be made with respect to how care isdelivered in a particular disease. For example, in this visualization,there are no isolated primary care physicians, suggesting that primarycare physicians will usually refer their diabetes mellitus type 1patients to a specialist for care.

Referring to FIG. 18, a method for describing, evaluating,understanding, or managing a network of health care providers mayinclude constructing a referral network database of physicians andhealth care providers from at least one of a private and a public datasource 1802, extracting data pertaining to shared patients or referralsbetween the physicians and health care providers from a database 1804,and generating a graphical representation of referral patterns in thereferral network of physicians and health care providers 1808, whereinat least one element of the graphical representation depicts a measureof an extent of a type of activity within the referral network. Theelement of the graphical representation may use at least one of size,thickness, color and pattern to depict a type of activity. The elementof the graphical representation may depict how many patients are sharedamong at least two health care providers. The medium may furthercomprise analyzing the referral patterns in the graphical representationto examine characteristics of the practice of the network and to enabledescribing, evaluating, understanding, or managing the network of healthcare providers 1810. The step of constructing a referral network ofphysicians and health care providers may use data mining techniques tofind relationship data between physicians and health care providers. Thestep of constructing a referral network of physicians and health careproviders may identify physicians and health care providers as nodeswith linkages in a referral network. The data sources may includeautomated collection and user-generated data sources for referralnetwork construction. The user-generated data may be from a survey. Thedata pertaining to shared patients or referrals may be extracted from aclaims or electronic health record database. The graphicalrepresentation may be an x-y coordinate system. Groups of physicians andhealth care providers may be differentiated in the graphicalrepresentation by at least one of a color, a shape, a shading, a sizeand the like. The size of the object representing the physicians orhealth care providers in the graphical representation may correlate witha metric. The metric may be at least one of cost, quality of care,compliance, or other measure of medical care, cost, resource use,quality, patient outcome and the like. In another embodiment, analyticaland visual tools may be used to examine the process of care. One suchtool may be based on heat maps, which may be a graphical representationof data where the values taken by a variable in a two-dimensional mapare represented as colors. Generating a heat map may include taking asequence of numeric values and representing them with color.

Heat maps may enable identifying similarities in clinics or other groupsof doctors in how they manage disease and generally provide care. Heatmaps may enable visualizing the organization of healthcare providersinto groups based on a similarity in providing care. In this way,healthcare providers may be identified as outliers or who may fit intosimilar groups. The heat map enables understanding the nature of a groupof healthcare providers and enables exploring the characteristics ofthat group.

Heat maps may be used to look to examine various elements of care, suchas co-morbidity, prescription use, and the like. For example, referringto FIG. 4, a heat map for Daily Encounter Volume may include taking asequence of numeric values associated with daily encounters indexed bydate, and representing them as a calendar with the days filled withcolors representing the values. In another example, referring to FIG. 5,co-morbidities with diabetes are represented graphically as a heat map.One axis of the map relates to diabetic status and the other axis of themap relates to other diseases or conditions, such as ophthalmicdisorders, PVD, Charlson co-morbidity score, stroke, CVD, renal disease,lipids, HTN, and the like. The colors of the plot may indicate thepresence of a co-morbidity, and in some embodiments, quantify theco-morbidity. In another example, referring to FIG. 6, a heat map fordiabetes prescribing patterns is shown. One axis of the map relates todiabetic status and the other axis of the map relates to prescribingpatterns, such as percentage of patients treated with alpha-glucosidaseinhibitors, percent treated with insulin, percent treated with insulinsecretagogues, percent treated with insulin sensitizers, averagetreatment values thereof, combinations thereof, and the like. The colorsof the plot visually indicate the quantifiable differences inprescribing for different diabetic statuses.

Another analytical and visual tool that may be used to examine theprocess of care may be parallel coordinate plots. Parallel coordinateplots are a unique way to look at patterns over time, such as by week,month, year, and the like. Parallel coordinate plots may be used toexamine the process of care for individuals in a patient-by-patient way.Each line of the plot may represent an individual patient. For example,referring to FIG. 7, patients, segmented into patients who arepre-diabetic, type I, type II, and type unknown, with a decrease of >1%in Hemoglobin A1c are shown in a parallel coordinate plot where eachline represents an individual patient. Levels of hemoglobin A1c aretypically used as indicators of diabetes disease management. HbAlclevels depend on the blood glucose concentration. That is, the higherthe glucose concentration in blood, the higher the level of HbAlc.Levels of HbAlc are not influenced by daily fluctuations in the bloodglucose concentration but reflect the average glucose levels over theprior six to eight weeks. Therefore, HbAlc is a useful indicator of howwell the blood glucose level has been controlled in the recent past andmay be used to monitor the effects of diet, exercise, and drug therapyon blood glucose in diabetic patients. For example, hemoglobin A1c(HbAlc) levels may be measured at the diagnosis and then measured againafter a period of treatment time to determine a change in hemoglobin A1cwith treatment. Along with these data points, other elements of care canalso be examined, such as number of endocrine visits, number oftherapies used, co-morbidity of ages, and the like. In the parallelcoordinate plot, a pattern may emerge of people who do well and peoplewho don't do well with respect to their care map. Referring to FIG. 8,patients with an increase of ≧1% in Hemoglobin A1c are shown in aparallel coordinate plot where each line represents an individualpatient. Referring to FIG. 9, a parallel coordinate plot profilingchange in HbAlc is shown. Referring to FIG. 10, only those patients whohad greater than or equal to five endocrinology encounters are shown onthe plot. Referring to FIG. 11, those patients who had an endocrinologyencounter are shown on the plot.

Referring to FIG. 12, a corrgram plot of physicians treating diabetes byoutcome and resource utilization is shown. Each column and rowrepresents a particular characteristic of clinical practice in the careof diabetes such as percent patients with renal failure, on insulin,with high LDL, and having type 1 diabetes, so that each characteristicis represented once vertically, and once horizontally. The ordering ofthe placement of each characteristic on the chart is determined by astatistical determination of the correlation of each characteristic withthe others in an analysis of many different physician practices. Highlycorrelated characteristics will be clustered together horizontally andvertically using a defined algorithm. The intensity of the correlationis indicated by the color of the boxes on the lower left half of thecorrgram at the intersection of each horizontal and verticalcharacteristic, with dark blue being most highly positively correlated,and dark red being most negatively correlated. The white lines withinthe boxes are a visual aid to demonstrate the direction of correlation,and help identify outlier boxes. The matrix in the upper right addsadditional information about the correlation, showing an ellipseencompassing 68% of the most concentrated data points from eachpractice, and a line indicating a loess smoothed curve of the datapoints from each practice. The corrgram can help to identify which careparameters are correlated with each other in the practices of physicianstreating patients with diabetes. For example, this corrgram demonstratesthat the percentage of patients in a practice who have a hospitaladmission is highly correlated with the average amount of inpatientcharges for this practice. Also, the percentage of a practice over theage of 65 is negatively correlated with the percentage who have high LDLlaboratory values.

Referring to FIG. 13, a heat map of doctors with 10+ actively manageddiabetes patients is shown. Each column represents a particularcharacteristic of a physician clinical practice in the care of diabetessuch as percent patients with renal failure, on insulin, with high LDL,having type 1 diabetes, with highest values in dark blue, medium valuesneutral, and lowest values in dark red for each measurement. Anindividual physician is represented by a row. The heat map isconstructed using a self-organizing clustering algorithm which clustersphysicians with similar characteristics clustered together vertically,and similar care characteristics clustered together horizontally. Thetree structure on the far left and top of the heat map indicate thedegree of similarity. The clinic to which each physician belongs iscolor coded as either blue or gray on the vertical column on the left ofthe heat map. Review of the heat map allows an administrator, physician,or nurse to identify similarities in the care of diabetes patients, andto identify key differences between individuals on particular careparameters. The heat map can identify similarities within and betweenclinics as well, and can identify physicians who fall outside the carecharacteristics of their particular clinic. Finally, by examining theclustering of care characteristics, the viewer can identify groups ofcare parameters that tend to be used in similar frequency by allphysicians. For example, the yellow/red cluster in the lower right areall indicators of charges, with these physicians submitting charges onthe lower end of their peers. An administrator could study otherparameters of care to see where they differ from their peers todetermine the reasons why their practices generate lower charges, onaverage, than other practices.

In an embodiment, the clinical analytics platform 100 may be embodied inthe network topology depicted in FIG. 21. This network topology includesa distributed, layered architecture comprising intrusion protection andintrusion detection systems. Such a topology provides the security andmanageability needed to deploy the platform 100 as a SaaS solution.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The processor may be part of aserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or include a signal processor, digital processor,embedded processor, microprocessor or any variant such as a co-processor(math co-processor, graphic co-processor, communication co-processor andthe like) and the like that may directly or indirectly facilitateexecution of program code or program instructions stored thereon. Inaddition, the processor may enable execution of multiple programs,threads, and codes. The threads may be executed simultaneously toenhance the performance of the processor and to facilitate simultaneousoperations of the application. By way of implementation, methods,program codes, program instructions and the like described herein may beimplemented in one or more thread. The thread may spawn other threadsthat may have assigned priorities associated with them; the processormay execute these threads based on priority or any other order based oninstructions provided in the program code. The processor may includememory that stores methods, codes, instructions and programs asdescribed herein and elsewhere. The processor may access a storagemedium through an interface that may store methods, codes, andinstructions as described herein and elsewhere. The storage mediumassociated with the processor for storing methods, programs, codes,program instructions or other type of instructions capable of beingexecuted by the computing or processing device may include but may notbe limited to one or more of a CD-ROM, DVD, memory, hard disk, flashdrive, RAM, ROM, cache and the like.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server and other variants such as secondaryserver, host server, distributed server and the like. The server mayinclude one or more of memories, processors, computer readable media,storage media, ports (physical and virtual), communication devices, andinterfaces capable of accessing other servers, clients, machines, anddevices through a wired or a wireless medium, and the like. The methods,programs or codes as described herein and elsewhere may be executed bythe server. In addition, other devices required for execution of methodsas described in this application may be considered as a part of theinfrastructure associated with the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the serverthrough an interface may include at least one storage medium capable ofstoring methods, programs, code and/or instructions. A centralrepository may provide program instructions to be executed on differentdevices. In this implementation, the remote repository may act as astorage medium for program code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe invention. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements.

The methods, program codes, and instructions described herein andelsewhere may be implemented on a cellular network having multiplecells. The cellular network may either be frequency division multipleaccess (FDMA) network or code division multiple access (CDMA) network.The cellular network may include mobile devices, cell sites, basestations, repeaters, antennas, towers, and the like. The cell networkmay be a GSM, GPRS, 3 G, EVDO, mesh, or other networks types.

The methods, programs codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on a peer topeer network, mesh network, or other communications network. The programcode may be stored on the storage medium associated with the server andexecuted by a computing device embedded within the server. The basestation may include a computing device and a storage medium. The storagedevice may store program codes and instructions executed by thecomputing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g. USB sticks or keys),floppy disks, magnetic tape, paper tape, punch cards, standalone RAMdisks, Zip drives, removable mass storage, off-line, and the like; othercomputer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/oror intangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flow charts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipments, servers, routers and the like.Furthermore, the elements depicted in the flow chart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps thereof, may berealized in hardware, software or any combination of hardware andsoftware suitable for a particular application. The hardware may includea general purpose computer and/or dedicated computing device or specificcomputing device or particular aspect or component of a specificcomputing device. The processes may be realized in one or moremicroprocessors, microcontrollers, embedded microcontrollers,programmable digital signal processors or other programmable device,along with internal and/or external memory. The processes may also, orinstead, be embodied in an application specific integrated circuit, aprogrammable gate array, programmable array logic, or any other deviceor combination of devices that may be configured to process electronicsignals. It will further be appreciated that one or more of theprocesses may be realized as a computer executable code capable of beingexecuted on a machine readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, each method described above and combinationsthereof may be embodied in computer executable code that, when executingon one or more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the invention has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present invention isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

All documents referenced herein are hereby incorporated by reference.

What is claimed is:
 1. A method of clinical tracking, comprising:gathering healthcare data from a plurality of sources; processing thehealthcare data, wherein processing comprises identifying, mapping andnormalizing healthcare data elements, wherein the mapping comprisesassigning data to a field of a database according to a hierarchicallyorganized lexicon of healthcare data elements, wherein multiple dataelement entries in the lexicon are mapped to a single field for at leastone field; analyzing the healthcare data to obtain at least one report;and presenting the report in a graphical user interface, wherein thereport can be customized based on a criterion.
 2. The method of claim 1,wherein the report identifies at least one risk relevant to at least onepatient based at least in part on the gathered healthcare data.
 3. Themethod of claim 1, wherein the report comprises an alert relating to atleast one risk associated with at least one patient based at least inpart on the gathered healthcare data, such alert presented in at leastone of an audible or visual manner.
 4. The method of claim 1, whereinthe report comprises an alert identifying at least one patient careerror and at least one recommendation for correcting such at least oneerror.
 5. The method of claim 1, wherein the report comprisesinstructions for the manner in which one or more healthcare providersare to provide care to one or more patients based at least in part onthe gathered healthcare data.
 6. The method of claim 1, wherein thereport identifies a disparity between the available healthcare resourcesand the patient needs identified based at least in part on the gatheredhealthcare data.
 7. The method of claim 1, wherein the report identifiesa high-cost patient based at least in part on the gathered healthcaredata.
 8. The method of claim 1, wherein processing the healthcare dataalso comprises validating the healthcare data elements.
 9. The method ofclaim 1, wherein the data are gathered on a periodic basis.
 10. Themethod of claim 1, wherein the data are gathered on a real-time basis,the report comprises instructions for the manner in which one or morehealthcare providers are to provide care to one or more patients basedat least in part on the gathered healthcare data and the report isupdated on a real-time basis.
 11. The method of claim 10, wherein thereal-time basis is at least as frequent as every five minutes.
 12. Themethod of claim 1, wherein the graphical user interface is presented viaa software-as-a-service architecture.
 13. The method of claim 1, whereinthe report relates to at least one of a patient, a medical careprotocol, an outcome, a demographic, a behavioral risk factor, a diseaserisk factor, a procedure, a therapeutic, a therapeutic over a given timeperiod, a risk level, a cost, an admission information, a utilization,readmission information, mortality, and a complication.
 12. The methodof claim 1, wherein the criterion comprises at least one of a patientname, an issue, a physician, a location, a due by time for care ortherapy, a risk level, a clinical measure, a procedure completed and animage taken.
 13. A method of optimizing a healthcare resource plan,comprising: gathering healthcare data relating to a plurality ofpatients from a plurality of sources, wherein the data are gathered on aperiodic basis; processing the healthcare data, wherein processingcomprises identifying, mapping and normalizing healthcare data elements,wherein processing is repeated when new healthcare data are gathered,wherein the mapping comprises assigning data to a field of a databaseaccording to a hierarchically organized lexicon of healthcare dataelements, wherein multiple data element entries in the lexicon aremapped to a single field for at least one field; analyzing thehealthcare data to obtain at least one patient risk identification andpatient tracking report, wherein analyzing is repeated when newhealthcare data are gathered and processed; and preparing a healthcareresource plan for care of the plurality of patients and optimizing thehealthcare resource plan based on the data contained in the at least onepatient risk identification and patient tracking report.
 14. The methodof claim 13, wherein the periodic basis is in real-time.
 15. The methodof claim 14, wherein the real-time basis is at least as frequent asevery five minutes.
 16. The method of claim 13, wherein processing thehealthcare data also comprises validating the healthcare data elements.17. The method of claim 13, further comprising, re-optimizing thehealthcare resource plan when new healthcare data are gathered,processed, and analyzed.
 18. The method of claim 13, further comprising,re-optimizing the healthcare resource plan when a manual change is madeto an element of the plan.
 19. The method of claim 13, wherein thetracking report relates to at least one of a patient, a medical careprotocol, an outcome, a demographic, a behavioral risk factor, a diseaserisk factor, a procedure, a therapeutic, a therapeutic over a given timeperiod, a risk level, a cost, an admission information, a utilization,readmission information, mortality, and a complication.
 20. The methodof claim 13, wherein patients at risk are automatically detected by theanalysis and an alert is generated identifying such patients.
 21. Themethod of claim 13, wherein high-cost patients are automaticallydetected by the analysis and an alert is generated identifying suchpatients.
 22. The method of claim 13, wherein the healthcare resourceplan is presented in a graphical user interface via asoftware-as-a-service architecture.